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2 votes
0 answers

Global optimizers handling minimization of an expression arising from the likelihood of a multivariate normal

I am interested in converting the following optimisation problem into a form that an exponential cone and/or SDP solver such as MOSEK can handle. This is a multivariate version of the question I ...
5 votes
2 answers

Global optimizers handling minimization of expressions like $\log{v}+\frac{1}{v}$

Consider the simple problem of maximum likelihood estimation of the variance of a mean zero normal distribution. The expression to be minimised is: $$N \log{v}+\frac{1}{v}\sum_{n=1}^N{b_n^2},$$ where $...
3 votes
1 answer

How do I pass an objective bound to Gurobi?

I have a non-convex Quadratic Programming over unite simplex set. I have a valid lower bound on the objective function (goal is minimization problem). If I add a constraint like $$f(x)\geq lower~bound,...
2 votes
0 answers

Does YALMIP allow a user-defined function for the objective function and constraints?

I have a robust optimization problem where the decision variable is a matrix, and the uncertain parameter is a vector. My matrix is L, and the uncertain parameter ...
0 votes
2 answers

How to normalize the objective functions of multi-objective optimization for a MPC?

I have a MPC with two objective functions, one that minimises fuel consumption and one that minimises the travel time of a vessel. I want to combine these two objectives into one weighted objective, ...
2 votes
1 answer

Benders decompositions: Number of iterations does not remain the same

I am solving an LP (i.e 118-bus system economic dispatch for 130% loading) using Benders decomposition. The problem takes 26 iterations to converge. This means that the process adds 25 cuts to the ...
6 votes
0 answers

Cases where RLT/SDP relaxation does not work well with standard quadratic optimization

(For people who don't know what RLT is): I am maximizing an indefinite quadratic function over a standard simplex, i.e., the standard quadratic optimization problem. A well-known approach is to relax ...
4 votes
1 answer

Maximizing 1-norm: using binary variables to relax non-convexity

It is well-known that when we maximize a 1-norm, e.g., $\|Ax\|_1$, we can use binary variables and obtain a mixed-integer convex problem (otherwise maximizing 1-norm is non-convex). I am mentioning ...